The growth of private 5G deployments has been phenomenal over the last decade. Enterprises are increasingly building their own wireless network infrastructure to support expanded coverage areas that demand reliable connectivity. Industries leveraging private 5G include:
All of them are leveraging the speeds and low latencies brought by 5G to create a web of wireless communications used to link scores of connected assets and human users on the ground [1],[2],[3].
This enables them to coordinate operations, enhance productivity, and monitor security and performance.
The use of deep packet inspection (DPI) in 5G is well documented, where its ability to provide real-time traffic intelligence at the user plane helps mobile network operators (MNOs) steer the network and manage traffic effectively and efficiently. In private 5G networks, DPI plays an even more crucial role, supporting large-scale enterprises in not only streamlining traffic flows onsite, but in managing hundreds of critical applications that underpin a growing share of operational processes
Before zooming into how DPI supports application management, it is worth looking into the advancements within DPI that enable it to deliver application-awareness in real time. The DPI engines, R&S®PACE2 and R&S®vPACE combine cutting-edge techniques such as machine learning (ML) and deep learning (DL) with statistical, behavioral and heuristic analysis to identify applications, protocols, and services accurately, and in real-time. Combined with metadata extraction, the DPI engines help enterprises and MNOs to profile, classify and analyze application traffic flows in full depth. Armed with application-level traffic intelligence, enterprises can monitor operational performance and track issues across their production value chain in each vicinity.
Key examples of how this works include:
Inventory management applications: A surge in traffic can indicate potential unauthorized activities in a production plant.
CCTV applications: Increased bandwidth usage may signal abnormal operations or security concerns.
Specific application features: Deviations in usage patterns, such as design file uploads or automated messages carrying production flow control instructions, can highlight anomalies.
This intelligence allows enterprises to:
With Encrypted Traffic Intelligence (ETI), a classification methodology that enables analyzing traffic flows that are encrypted, anonymized, or obfuscated, enterprises gain expanded visibility into onsite network activities. This ensures that new and emerging applications, even those encrypted with increasingly stringent protocols, remain on the enterprise’s radar. furthermore the capability of custom classification of signatures, allows enterprises to track proprietary applications tailored for specific industries and use cases.
Application-level visibility also helps enterprises streamline each application—from ensuring sufficient compute resources to improving application architectures including distribution of workloads across edge, on-premises and the cloud. It also helps enterprises to rapidly diagnose issues in the code, middleware, or storage layers by observing packet behavior and application response times.
Beyond application monitoring, enterprises managing their own private 5G networks can leverage DPI to manage network performance. Either deployed in an edge node close to a cluster of IoT assets, or in the RAN, or in the network core hosted on-premises or in the cloud, DPI is a powerful technology to capture minute changes and degradation in network performance. Instead of parsing performance issues in large, complex data sets, DPI immediately flags typical network abnormalities such as:
By analyzing these signals in real time, DPI provides comprehensive traffic insights that significantly accelerate issue detection and root-cause diagnosis. These insights can be used to uncover issues in any network domain—from underperforming CUs and DUs in RAN to congestion and signaling inefficiencies in core functions such as the PCF, AF and NEF. They also help to surface bottlenecks in supporting cloud-native infrastructure such as load balancers.
Network equipment issues, for example power issues, frequent downtimes and unusual consumption of bandwidth can also be deduced from DPI analysis. Similarly, across end devices, information on IP addresses, sessions and transmission activity enables enterprises to detect and monitor both authorized and unauthorized moving objects, as well as other fixed connected hardware tapping into a private 5G network. In cases where enterprises manage their private 5G networks end-to-end, this information forms the first layer of alerting to ensure communications remain secure, particularly in mission-critical sectors such as emergency services and energy infrastructure — supporting both resilience and cybersecurity.
A private 5G network involves significant overheads, which is why enterprises are focused on continuous optimization. In this case, DPI data can be used to execute several optimization strategies that reduce operational expenditure while maintaining performance. Among the most impactful examples are:
Together, these strategies enable enterprises to not only manage network traffic efficiently, but to maximize the value of high-performance private 5G deployments while avoiding excessive resource consumption. By providing continuous performance insights across both private and shared infrastructure models, DPI ensures that traffic orchestration remains reliable, cost-efficient and fully aligned with application and user requirements — even in complex environments such as airports, transit hubs or 5G-as-a-Service (5GaaS) deployments.
With enterprises today handling sensitive data governed by privacy and protection regulations such as HIPAA, GDPR, and CCPA, it is essential that private 5G networks can identify and filter transactions involving the access, retrieval, and movement of such data.
Through fine-grained application awareness, DPI enables enterprises to enforce strict rules on how sensitive information is accessed and used, ensuring health records, financial details, and other personally identifiable information (PII) is never compromised or tampered with. This deep visibility into the application layer also supports data sovereignty, helping enterprises ensure that data remains within approved geographic or organizational boundaries. For instance, in a 5G-enabled smart energy network handling information (grid control data, fault reports, and sensor telemetry) from critical national infrastructure, information may be processed at the edge, on-premises, or within a private data center. DPI ensures that any communication between these nodes and external networks or clouds does not lead to leakage or unauthorized transfer of sensitive information. This approach safeguards regulatory compliance and maintains operational continuity.
The exponential growth of connected assets and the use of heterogeneous architectures—combining public and private LTE and 5G, FWA, and Wi-Fi—are driving higher network densities, creating new challenges for enterprises deploying their own private wireless networks. These challenges are further amplified by an app-centric era, where every process on the ground is digitalized and managed remotely, increasing dependency on last-mile networks. The use of DPI equips enterprises with the insights needed to:
Looking ahead, as 5G evolves toward 6G, enterprises will face greater requirements across all fronts, including intelligent automation. With DPI, enterprises can be confident that they are not just keeping up—but staying ahead in the evolution of private 5G.
As the Head of Customer Projects & Product Development, Oliver manages the research and development of new and existing products as well as various customer projects. He has more than 15 years of leadership experience. Prior to joining ipoque, Oliver advised different clients on marketing and advertising. He also managed a team of different specialists creating digital software solutions. Outside of work, he enjoys cooking and running.
